Measuring dynamic and absolute displacement

ABSTRACT

Disclosed are methods of measuring dynamic displacement of an object. The methods include capturing a plurality of images of a dynamically moving object. The object may include one or two color-patterned target(s) that include two rows and two columns of two contrasting colors (e.g., colors with a difference in hue values of approximately 180). The methods include identifying a reference point of the object (e.g., a center of a color-patterned target or a midpoint between the centers of two color-patterned targets) and measuring the dynamic movement (displacement) of the reference point over time. The images may be captured by a smartphone camera and the smartphone may identify and measure the dynamic movement of the reference point. In view of the constraints of smartphone hardware capabilities, the images may be cropped and/or not displayed to enable the smartphone to process the images and measure the dynamic displacement in real time.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Prov. Pat. Appl. No.62/167,546, filed May 28, 2015, which is incorporated herein byreference in its entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

Not applicable

BACKGROUND

The process of implementing a damage detection and characterizationstrategy for engineering structures is generally referred to asstructural health monitoring (SHM). When permanent deformation occurs,structural displacement or deformation information is particularlyimportant and it is often more accurate than acceleration measurementsin lower-frequency ranges.

Conventional methods for displacement monitoring, which includeGPS-based methods and computer vision-based methods, each have their owndrawbacks. The cost for survey-level, dual-frequency GPS systems thatsupport sub-centimeter accuracy is still too high for routine use.Meanwhile, single-frequency, low-cost GPS devices that are generallyused for navigation purposes are not sufficiently advanced for practicaluse in SHM applications.

Computer vision-based methods use a video camera (generally incombination with an optical zoom lens and lighting lamps) that captureimages of a precision target attached to a location of interest on astructure over time. Non-contact, vision-based systems are available atrelatively low cost and significantly reduce the difficulties inproviding stationary reference points (a critical challenge forcontact-type displacement sensors). Conventional video cameras, however,have a number of drawbacks. The limited resolution of conventional videocameras causes difficulty in identifying high-frequency displacements,which have smaller amplitudes than low-frequency vibrations. At higherresolutions, the maximum frame rates of conventional video cameras arelimited to 30-60 frames per second. While those low frame rates may besufficient for measuring low-frequency and high-amplitude vibrations oflong-period structures such as high-rise buildings and long-spancable-supported bridges, higher frame rates are essential forappropriately monitoring the dynamic behavior of many of small-to-midscale structures. Additionally, anti-aliasing filters are not availablefor conventional vision-based measuring systems. While a high-speedcamera (allowing up to 2000 frames per second) may minimize suchaliasing problems, the practical use of such expensive cameras for civilengineering applications is still in question because of the level ofcost and the difficulty of achieving real-time processing.

Recent advances in smartphone technologies provide various onboardsensing capabilities. In particular, the embedded cameras included inmany smartphones provide higher resolution images and higher frame ratesthan many conventional video cameras. Moreover, their powerfulprocessors and memories allow for onboard processing capabilities,eliminating the need for additional computers to perform extensive imageprocessing. Meanwhile, because of their many general purpose functions(e.g., cellular telephony, text messaging, or internet access, etc.),smartphones are nearly ubiquitous.

Accordingly, there is a need to measure dynamic and absolutedisplacement by a smartphone.

While the processing power of currently-available smartphones isincreasing, smartphone capabilities are still limited when compared todesktop computing devices that can include hardware and softwareselected for their proficiency regarding computer vision applications.Therefore, in order to quickly and accurately measure bothlow-frequency, high-amplitude vibrations and high-frequency,low-amplitude vibrations using currently-available smartphonetechnology, the process must be optimized.

SUMMARY

According to an aspect of an exemplary embodiment, there is provided amethod of measuring dynamic and absolute displacement by a smartphone,including capturing a plurality of images of a dynamically movingobject, identifying a reference point of the dynamically moving objectin the plurality of images, and measuring dynamic displacement of thereference point over time.

According to an aspect of another exemplary embodiment, there isprovided a non-transitory computer readable storage medium that storesinstructions that, when executed by one or more smartphone processors,cause the smartphone to capture a plurality of images of a dynamicallymoving object, identify a reference point of the dynamically movingobject in the plurality of images, and measure dynamic displacement ofthe reference point over time.

According to an aspect of another exemplary embodiment, there isprovided a smartphone (including non-transitory computer readablestorage media and one or more processors) that captures a plurality ofimages of a dynamically moving object, identifies a reference point ofthe dynamically moving object in the plurality of images, and measuresdynamic displacement of the reference point over time.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of exemplary embodiments may be better understood with referenceto the accompanying drawings. The components in the drawings are notnecessarily to scale, emphasis instead being placed upon illustratingthe principles of exemplary embodiments, wherein:

FIG. 1 is a diagram illustrating a color-patterned targets and accordingto an exemplary embodiment of the present invention;

FIG. 2 is a block diagram illustrating a smartphone according to anexemplary embodiment of the present invention;

FIG. 3 are diagrams that illustrate a process for identifying acolor-patterned target according to an exemplary embodiment of thepresent invention.

FIG. 4 is a flowchart illustrating a process for measuring dynamic andabsolute and dynamic displacement according to an exemplary embodimentof the present invention.

FIG. 5 are views of a graphical user interface according to an exemplaryembodiment of the present invention;

FIG. 6 is a graph illustrating the sampling time for 60 fps and 120 fpscases according to a validation of an exemplary embodiment;

FIG. 7 is a graph illustrating the shake table test results according toa validation of an exemplary embodiment; and

FIG. 8 is a graph illustrating outdoor shaking table tests according toa validation of an exemplary embodiment.

DETAILED DESCRIPTION

Reference will now be made in detail to exemplary embodiments by way ofreference to the accompanying drawings, wherein like reference numeralsrefer to like parts, components, and structures.

FIG. 1 is a diagram illustrating color-patterned targets 100 a and 100 baccording to an exemplary embodiment of the present invention. In orderto measure the dynamic and absolute displacement of an object (e.g., astructure), one or more color-patterned targets 100 a and 100 b may beaffixed to the structure. As shown in FIG. 1, for example, thecolor-patterned target 100 a may include four quadrants 111 a, 112 a,113 a, and 114 a, arranged in two columns and two rows, with a centerpoint 120 a at the intersection of the four quadrants 111 a-114 a.Similarly, the color-patterned target 100 b may include four quadrants111 b, 112 b, 113 b, and 114 b, arranged in two columns and two rows,with a center point 120 b of the four quadrants 111 b-114 b. Themidpoint between the center point 120 a and the center point 120 b isidentified by reference number 130.

In order for a smartphone to accurately identify the location of thecenter point 120 a, the quadrants 111 a-114 a may be chosen such thateach column and each row includes two contrasting colors. (For example,quadrants 111 a and 114 a may be a first color and quadrants 112 a and113 a may be a second color that contrasts with the first color.)Similarly, the quadrants 111 b-114 b may be chosen such that each columnand each row includes two contrasting colors. In order for a smartphoneto accurately identify each of the color-patterned targets 100 a and 100b, the colors of the color-patterned target 100 b may be different thanthe colors of the first color-patterned target 100 a. (For example,quadrants 111 b and 114 b may be a third color and quadrants 112 b and113 b may be a fourth color that contrasts with the third color.)

The contrasting colors may be selected based on the hue values of thosecolors. Hue values are represented on a 360 degree scale as shown inTable 1:

Hue Color  0 degrees Red  60 degrees Yellow 120 degrees Green 180degrees Cyan 240 degrees Blue 300 degrees Magenta

For the purposes of this disclosure, colors may be “contrasting” if theyhave a difference in hue values of between 150 and 210 degrees, morepreferably between 170 and 190 degrees, more preferably approximately(i.e., ±5 degrees) 180 degrees. To use one specific example, each rowand each column of the color-patterned target 100 a may include oneyellow quadrant (e.g., quadrants 111 a and 114 a) and one blue quadrant(e.g., quadrants 112 a and 113 a) and each row and each column of thecolor-patterned target 100 b may include one green quadrant (e.g.,quadrants 111 b and 114 b) and one magenta quadrant (e.g., quadrants 112b and 113 b).

FIG. 2 is a block diagram illustrating a smartphone 200 according to anexemplary embodiment of the present invention. As shown in FIG. 2, thesmartphone 200 includes one or more processors 220, memory 240, a camera260, and a display 280. The smartphone 200 may also include a hardwareinterface 290.

The one or more processors 220 may include a central processing unit 222and a graphics processing unit 224. The central processing unit 222 maybe any suitable device that carries out the instructions of a computerprogram by performing the arithmetic, logical, control and input/output(I/O) operations specified by the instructions. The graphics processingunit 224 may be a specialized electronic circuit designed to rapidlymanipulate and alter memory to accelerate the creation of images in aframe buffer. The graphics processing unit 224 may process blocks ofvisual data in parallel. The central processing unit 222 and a graphicsprocessing unit 224 may be realized as a single semiconductor chip ormore than one chip.

The memory 240 may include any non-transitory computer readable storagemedia (e.g., a hard disk, solid-state memory, etc.) that storesinstructions (i.e., computer programs) that, when executed by the one ormore processors 222 and/or 224, cause the smartphone 200 to perform thefunctions described herein.

The camera 260 may be any suitable remote sensing device that capturessequences of images for digital storage in the memory 220. The camera260 includes an images sensor that detects and conveys the informationthat constitutes each of the images. The image sensor, for example, mayhave a graphical resolution of 8 megapixels or greater. The camera 260preferably captures images with a resolution of 720 p (i.e., ahorizontal resolution of 720 pixels and a vertical resolution of, forexample, 1280 pixels), more preferably with a resolution of 1080p (i.e.,a horizontal resolution of 1080 pixels and a vertical resolution of, forexample, 1920 pixels), even more preferably with a resolution of 4K(i.e., a horizontal resolution on the order of 4,000 pixels), etc.Depending on the resolution, the camera 260 preferably capture imageswith a frame rate of 30 frames per second, more preferably with a framerate of 60 frames per second, even more preferably with a frame rate of120 frames per second, etc. The images captured by the camera 260 may bestored in the memory 240, processed by the CPU 222 and/or the GPU 224,displayed by the display 280, and/or transferred to an externalcomputing device via the hardware interface 290.

The display 280 may be any suitable output device that presentsinformation, such as the images captured by the camera 260, in visualform. The display 280 may be, for example, a liquid crystal display(LCD), a light emitting polymer display (LPD), an light emitting diode(LED) display, an organic light emitting diode (OLED) display, etc.

The hardware interface 290 may be any suitable interface to enable thesmartphone 200 to send to and receive data from an external computingdevice such as a personal computer. The hardware interface may be, forexample, a universal serial bus (USB) port, a thunderbolt port, alightning port, a FireWire port, etc.

The smartphone 200 includes computer programs that are stored in thememory 240 and executed by the CPU 222 and/or the GPU 224. The computerprograms include an operating system, such as the iOS mobile operatingsystem, which is developed by Apple Inc., or the Android mobileoperating system, which is developed by Google Inc. The computerprograms may also provide functionality, for example, for a user toplace telephone calls, to send and receive text messages (e.g., shortmessage service or SMS), multi-media messages (MMS), email messages,etc., to access the internet, etc.

The smartphone 200 also includes computer programs to process imagessuch as color adjustment filters (e.g., an RGB level filter, a hue levelfilter, a luminance threshold filter, etc.) and image processing filters(a Harris corner detection filter, a Sobel edge detection filter, aHough transform line detection filter, etc.). The color adjustmentfilters and/or the image processing filters may be selected from an opensource library of computer vision applications, such as the OpenCVlibrary, which was originally developed by Intel, or the Open Graphicslibrary (OpenGL), which was originally developed by Silicon Graphics.The computer programs may also provide the user with functionality tocrop images captured by the camera 260.

To track the objects using computer vision, the smartphone 200 detectsthe objects of interest in a video sequence captured by the camera 260,classifies the detected objects, and tracks the movements of identifiedtargets. In order to minimize processing time, which is necessary torealize a higher frame rate for dynamic displacement monitoring, thesmartphone 200 may use a contour/edge-based method to track thecolor-patterned targets 100 a and/or 100 b. Alternatively, thesmartphone 200 may use another method, such as a region-based orpoint-based method.

FIG. 3 are diagrams that illustrate a process for identifying acolor-patterned target 100 a and determining a center point 120 a of thecolor-patterned target 100 a according to an exemplary embodiment of thepresent invention. FIG. 3 includes representations of thecolor-patterned target 100 a and matrices 300 a-e, which include cells311-319.

The color-patterned target 100 a may be identified in an image, forexample, by converting RGB values of pixels to hue values and filteringthe images based on whether the hue values are within thresholds thatare predetermined to encompass the hue values of the color-patternedtarget 100 a.

A center point of a color-patterned target 100 a may be determined bysampling a pixel location and determining the hue values of the sampledpixel and the neighboring pixels in eight directions of the sampledpixel. The hue value of the sampled pixel is shown in cell 315 and thehue values of the neighboring pixels are shown in cells 311-314 and316-319.

The GPU 224 processes an image that includes the color-patterned target100 a by highlighting pixels near the center of the color-patternedtarget 100 a and not highlighting pixels that are not near the center ofthe target. The CPU 222 loads the processed image into memory and findsall of the pixels that were highlighted. The CPU 222 determines thecenter point 120 a of the color-patterned target 100 a by averaging thetwo-dimensional coordinates of each highlighted pixel.

FIG. 4 is a flowchart illustrating a process 400 for measuring dynamicand absolute displacement according to an exemplary embodiment of thepresent invention. The process 400 may be performed by the smartphone200.

Images of a dynamically moving object are captured in step 410. Theimages may be captured by the camera 260 of the smartphone 200. Thedynamically moving object may be, for example, the color-patternedtarget 100 a, which may be affixed to a structure. As described above,the color-patterned target 100 a may include two rows and two columns,each column and each row including two contrasting colors (e.g., colorshaving a hue value difference of approximately 180 degrees).Alternatively, the dynamically moving object may be two color-patternedtargets 100 a and 100 b, which may be affixed to a structure. The imagesmay be captured by the smartphone at a frame rate of 120 images persecond or more. The frame rate may be set by the user or predeterminedand stored in the instructions on the smartphone 200. The images arecaptured by the smartphone at a resolution of 720 p or greater. Theresolution may be set by the user or predetermined and stored in theinstructions on the smartphone 200.

The images are be processed in step 420. The images may be processed bythe smartphone 200 (For example, by the GPU 224). As described above,the smartphone 200 may process the image by converting RGB values ofpixels to hue values and filtering the images based on whether the huevalues are within predetermined thresholds.

The images (and or graphical representations based on the images) may bedisplayed in step 422. The images may be displayed, for example, via thedisplay 280 of the smartphone 200.

The images may be cropped in step 424. The images may be cropped by thesmartphone 200. The images may be cropped, for example, such that thevertical resolution of each image may be 100 pixels. The horizontaland/or vertical resolution of the cropped images may be set by the useror predetermined and stored in the instructions on the smartphone 200.

One or more color-patterned targets 100 a, 100 b, etc. are identified inthe captured images captured in step 430. The one or morecolor-patterned targets 100 a, 100 b, etc. may be identified, forexample, by determining whether hue values of pixels are withinthresholds that are predetermined to encompass the hue values of thecolor-patterned targets 100 a, 100 b, etc.

A reference point is identified in the images in step 440. The referencepoint may be identified by the smartphone 200. The reference point maybe, for example, the center point 120 a of the color-patterned target100 a. Alternatively, the reference point may be the midpoint 130between the center points 120 a and 120 b. The smartphone 200 mayestimate the center point 120 a and/or 120 b by identifying pixels nearthe center of the color-patterned target 100 a and/or 100 b anddetermining an average location of the pixels near the center of thecolor-patterned target 100 a and/or 100 b. The pixels near the center ofthe color-patterned targets 100 a and/or 100 b may be identified byconverting red-green-blue (RGB) values of image pixels to hue values anddetermining whether the hue values are within a predetermined rangeusing a hue value filter.

Displacement of the reference point is calculated in step 450. Thedisplacement of the reference point may be calculated by the smartphone200.

Information indicative of the dynamic displacement of the referencepoint over time is output in step 460. The information may be output thesmartphone 200. The smartphone 200 may display the information via thedisplay 280. For example, by the smartphone may display a graph of thedisplacement of the reference point over time. Additionally oralternatively, the smartphone 200 may output the information to anexternal computing device via the hardware interface 290.

After outputting information indicative of the dynamic displacement ofthe reference point in output in step 460, the process 400 may return tostep 410 in order to continue determining the dynamic displacement ofthe reference point over time.

In order for a smartphone 200 to identify one or more color-patternedtargets 100 a in a camera frame and calculate its displacement at eachframe within a certain time period (e.g., 10 milliseconds on the iPhone6 Plus), the process 500 may be optimized. For example, the softwareinstructions may be such that the smartphone 200 does not display theimages captured by the camera 260 on the display 280. In anotherexample, the software instructions may be such that all operationsrelated to the camera 260 are run on a separate thread and all userinterface operations are run on the main thread.

FIG. 5 are views 500 a and 500 b of a graphical user interface accordingto an exemplary embodiment of the present invention. The views 500 a and500 b may be output, for example, by the smartphone 200 via the display280.

As shown in FIG. 5, the view 500 a includes an image 510 a and a graph540 a. As shown in FIG. 5, the view 500 b similarly includes an image510 b and a graph 540 b. The views 500 a and 500 b may also includeicons (for example, a start icon 550 and a settings icon 560). Theimages 510 a and 510 b indicate the locations of the color-patternedtargets 100 a and 100 b affixed to a structure. The images 510 a and 520b may be pictures captured by the camera 260, processed by the one ormore processors 220, and output by the display 480. Alternatively, theimages 510 a and 520 b may be graphical representations of thecolor-patterned targets 100 a and 100 b rendered by the smartphone 200based on the pictures captured by the camera 260.

In order to determine the dynamic displacement of the structure, thesmartphone identifies the location of the center points 120 a and 120 bof the color-patterned targets 100 a and 100 b, and a midpoint 130between the center points 120 a and 120 b. As shown in FIG. 5, thesmartphone 200 may also superimpose a first dot 520 a indicating thelocation of the center point 120 a, a second dot 520 b indicating thelocation of the center point 120 b, and a third dot 530 indicating thelocation of the midpoint 130 between the center points 120 a and 120 b.As shown in FIG. 4B, the smartphone 200 may also superimpose a fourthdot 532 indicating the original location of the midpoint 130 in additionto the first dot 520 a, the second dot 520 b, and the third dot 530.

The graph 540 b shows the dynamic displacement of the reference pointover time. The graphical user interface may also display the averageprocessing time for the smartphone to capture, filter, and display theimage.

The graphical user interface may provide functionality for a user toadjust the setting of the software application. The user adjustablesettings may include camera settings, filter settings, and graphsettings. The camera settings may provide functionality for the user toadjust the frame rate (e.g., 30, 60, 120 and 240 frames per second,etc.), the crop size (e.g., a horizontal resolution of 100, 200, 400,720, or 1280 pixels and/or a vertical resolution of 100, 200, 400, or720 pixels, etc.), the auto focus (e.g., near, far, none), whether tooutput the images captured by the camera 260 via the display 280, and/orwhether to display the processing time. The filter settings may providefunctionality for the user to determine whether the resulting outputfrom the OpenGL shader should be shown in place of the raw camera view,to determine the distance between neighboring pixels used in the OpenGLshader, and to select the hue ranges of the two colors used in eachtarget. The graph settings may provide functionality for the user todetermine whether the graph should be updated with displacementcalculations from each frame, the distance (in centimeters) of the twocolored targets in the camera frame, and whether the X, Y, or bothdisplacement lines should be calculated and shown on the graph.

Experimental Validations

A new smartphone application was developed under iOS environment for theiPhone. A region/color-based tracking method was adapted because of itscomputational efficiency in image processing and robustness in trackingfast moving objects. In order to fully utilize the GPU capabilities ofsmartphones, the GPUImage library was used in developing the iOS app. Acrop filter was implemented for users to compromise between the imagesize and frame rate without sacrificing accuracy. Onboard calibration ofthe image pixel size to a given-dimension target was implemented in thedeveloped iOS app. And other various features for controlling camera,filter, and graph settings and email transmission of measured data werealso incorporated in this iOS app development. In order to evaluate theperformances of the iOS application, including sampling time accuracyand the ability to track the dynamic movements of targets, a series oflaboratory-scale tests were carried out using a shaking table withsingle-frequency and multi-frequency sinusoidal motions. All functionsrequired for measuring the dynamic movements of the target weresuccessfully be operated in real time, allowing up to 120 frames persecond (fps) with an iPhone 6 Plus. The performances of the iPhonehardware and the iOS application were experimentally validated.

An APS Dynamics' APS 400 Electro-Seis shaker was used for the evaluationtests. The input excitation of the shaker was amplified by the APS 145amplifier. To compare the performance of the developed iOS App with thatof a conventional displacement sensor, a laser displacement sensor(KEYENCE, IL-100, 4-μm resolution) was used as a reference. The analogvoltage outputs from the laser sensor were measured by a NationalInstruments' NI-9234 ADC module (24-bit Delta-sigma ADC) with CompactDAQchassis (cDAQ-9178 model). At the same time, a NI-9269 voltage outputmodule, which was housed in the same CompactDAQ, was used to generatethe excitation signals for the shaker. To overcome the limitedresolution of the iPhone camera for long-distance and small-targetmeasurements, a 50× optical zoom lens was used in conjunction with theiPhone. (Low-cost 12× and 50× optical zoom lens are commerciallyavailable with precisely designed smartphone cover cases that allow easyconnection of the lens to the phone.)

Consistency of the sampling rate or sampling time is important to ensurethe quality of dynamic vibration measurements. FIG. 6 shows the examplerecord of the sampling time for 60 fps and 120 fps cases (720×100p cropfilter used for both). The case with 60 fps (dotted line) shows veryconsistent sampling time of 16.67 milliseconds over entire measurements.However, when 120 fps (solid line) was used, little inconsistencies areobserved in the beginning of the measurements for a couple of thesamples, of which phenomenon is attributed by the dropped samples (seethe bottom of FIG. 6). To achieve 120 fps, all the image processingrequired to get the displacement information should be done within 8.33ms for each frame. If the processing takes more than 8.33 ms, then thesoftware automatically drops the corresponding sample out, to not causeany delay or interference to following samples. Because the case of 60fps ensures sufficient time for processing, such dropped samples werenot observed in this test.

For the initial shake table tests indoors, the iPhone with the zoom lenswas placed 3.0 meters away from the target attached on the shake table.The target size was 1.0×2.5 cm, which was composed of two rectangularalternating color patterns having 1.5 cm center distance between them. A720×100p crop filter was used to track the target in a horizontaldirection in an optimized way. The distance between the two colorpatterns (i.e. 1.5 cm) was occupied by about 300˜400 pixels. Acorresponding resolution for this particular set up could be estimatedat about 0.0375˜0.05 mm; actual size of each pixel was autonomouslycalibrated in the software and used for displacement calculation.

FIG. 7 shows the shake table test results for the (a) 1 Hz, (b) 10 Hz,and (c) 20 Hz sinusoidal excitations as well as (d) multi-toneexcitation composed of 1-20 Hz (0.2 Hz step) sinusoidal signals.Vibration levels were kept below 2 mm amplitude (peak to peak), and 120fps was used in this test. As shown in the FIG. 7, the dynamicdisplacements measured by the iPhone 6 Plus with the developed iOS app(solid line in the Figure) agree very well with those of the laserdisplacement sensor (dotted line in the Figure).

The shake table set ups were moved outdoors for outdoor testing withlonger target distance. The shake table set ups were placed in theoutdoor hallway of the civil engineering building at the University ofArizona, of which hallway can ensure up to 50 m clear line-of-sight.Target distance from the iPhone camera was 33 m and the same zoom lenswas used, but with little bigger target (4×10 cm target size and 6 cmcenter distance between two color patterns).

FIG. 8 shows some example results from the outdoor shaking table tests(33 m from target): (a) 5 Hz sine at 120 fps, (b) 5Hz sine at 60 fps,(c) multi-tone sine at 120fps, and (d) multi-tone sine signal at 60 fps.The performances of the iPhone with the developed app were not soimpressive, compared with indoor tests. Particularly when 120 fps wasused, substantial high-frequency noises were observed in themeasurements by iPhone (solid line in the Figure) as shown in the FIG. 8(a) and (c), while the results from 60 fps were acceptable, successfullyresolving millimeter-level displacements. Possible reasons for thesehigh-frequency noises in outdoor tests may be attributed to thepossibility that the captured image at 120 fps might be exposed to lessamount of light as the higher frame rate allows the shorter exposuretime, which could change the color properties in the image, thepossibility that the smartphone might be subjected to unexpectedhigh-frequency vibrations due to wind and/or building vibrations,resulting in such noisy measurements (though it is a very littlevibration, its effects on the captured images would be substantial asthe target is located further and further away). No matter what thereasons for causing such high-frequency noises, possible vibrations ofthe phone itself should be compensated for the practical use of thisapproach for dynamic displacement measurements in the field. Othersensors (e.g., accelerometer, gyroscope) embedded in the smartphone maybe utilized for the phone vibration compensation. To ensure sufficientamount of light for outdoor tests, a self-light emitting target (e.g.,LED) may be used for future tests. In addition, a low-pass filtering canbe implemented in the iOS app to reduce such high-frequency noises.

Although some high-frequency noises were observed from outdoorshake-table tests, the performances of the developed application werecomparable to those of a conventional laser displacement sensor,allowing down to sub-millimeter resolutions at 33 m distance from thetarget.

The foregoing description and drawings should be considered asillustrative only of the principles of the inventive concept. Exemplaryembodiments may be realized in a variety of shapes and sizes and are notintended to be limited by the preferred embodiments described above.Numerous applications of exemplary embodiments will readily occur tothose skilled in the art. Therefore, it is not desired to limit theinventive concept to the specific examples disclosed or the exactconstruction and operation shown and described. Rather, all suitablemodifications and equivalents may be resorted to, falling within thescope of this application.

What is claimed is:
 1. A method of measuring dynamic displacement, themethod comprising: capturing, by a smartphone, a plurality of images ofa dynamically moving object; identifying, by the smartphone, a referencepoint of the dynamically moving object in the plurality of images; andmeasuring, by the smartphone, dynamic displacement of the referencepoint over time.
 2. The method of claim 1, wherein the reference pointis identified by a graphics processing unit of the smartphone.
 3. Themethod of claim 1, wherein the dynamically moving object includes atleast one pattern with two rows and two columns, each column and eachrow including two contrasting colors.
 4. The method of claim 3, whereinthe contrasting colors have a hue value difference of approximately 180degrees.
 5. The method of claim 3, wherein identifying the referencepoint includes estimating a center of the at least one pattern.
 6. Themethod of claim 5, wherein estimating the center of the at least onepattern includes identifying pixels near the center of the at least onepattern and determining an average location of the identified pixels. 7.The method of claim 6, wherein identifying pixels near the center of theat least one pattern comprises: converting pixel red-green-blue (RGB)values to hue values; and determining whether pixel hue values arewithin a predetermined range.
 8. The method of claim 1, wherein: thedynamically moving object includes: a first pattern including two rowsand two columns, each column and each row including a first color andsecond color contrasting with the first color; and a second patternincluding two rows and two columns, each column and each row including athird color and a fourth color contrasting with the third color; andidentifying the reference point includes: estimating a center of thefirst pattern; estimating a center of the second pattern; and estimatinga midpoint between the center of the first pattern and the center of thesecond pattern.
 9. The method of claim 1, wherein the images arecaptured by the smartphone at a frame rate of 120 images per second ormore.
 10. The method of claim 1, wherein the images are captured by thesmartphone at a frame rate set by a user via the smartphone.
 11. Themethod of claim 1, wherein the images are captured by the smartphone ata resolution of 720 p or greater.
 12. The method of claim 1, wherein theimages are captured by the smartphone at a resolution set by a user viathe smartphone.
 13. The method of claim 1, further comprising croppingthe plurality of images.
 14. The method of claim 13, wherein the imagesare cropped such that one dimension of each cropped image is 100 pixels.15. The method of claim 13, wherein the images are cropped such that atleast one dimension of each image is set by a user via the smartphone.16. The method of claim 1, further comprising: outputting, by thesmartphone, the displacement of the reference point over time.
 17. Themethod of claim 1, further comprising: graphing, by the smartphone, thedisplacement of the reference point over time.
 18. The method of claim1, wherein the smartphone is configured to provide functionality forcellular telephony, text messaging, or internet access.
 19. Anon-transitory computer readable storage medium that stores instructionsthat, when executed by one or more smartphone processors, cause thesmartphone to: capture a plurality of images of a dynamically movingobject; identify a reference point of the dynamically moving object inthe plurality of images; and measure dynamic displacement of thereference point over time.
 20. A smartphone that measures displacementof a dynamically moving object, the smartphone comprising: one or moreprocessors; and non-transitory computer readable storage medium thatstores instructions that, when executed by the one or more smartphoneprocessors, cause the smartphone to: capture a plurality of images of adynamically moving object; identify a reference point of the dynamicallymoving object in the plurality of images; and measure dynamicdisplacement of the reference point over time.